Coronavirus US - USA Facts
davegoblue
2023-02-08
Background
This module builds on code contained in Coronavirus_Statistics_USAF_v007.Rmd. This file includes the latest code for analyzing data from USA Facts. USA Facts maintains data on cases and deaths by county for coronavirus in the US. Downloaded data are unique by county with date as a column and a separate file for each of cases, deaths, and population.
The intent of this code is to move updated functions to sourcing files and to better manage memory.
Sourcing Functions
The tidyverse library is loaded, and the functions used for CDC daily processing are sourced. Additionally, specific functions for USA Facts are also sourced:
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.1 ✔ tibble 3.1.8
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v002.R")
source("./Coronavirus_USAF_Functions_v002.R")Further, the mapping file specific to USA Facts is sourced:
# Updated to handle length-zero inputs - also in CDC Daily v005
# Generic function to rename columns in a file using an input vector
colRenamer <- function(df,
vecRename=c(),
...
) {
# FUNCTION ARGUMENTS:
# df: the data frame or tibble
# vecRename: vector for renaming c('existing name'='new name'), can be any length from 0 to ncol(df)
# ...: additional arguments to be passed to rename_with
# Rename the columns as requested
if(length(vecRename)>0) dplyr::rename_with(df, .fn=function(x) vecRename[x], .cols=names(vecRename), ...)
else df
}
source("./Coronavirus_USAF_Default_Mappings_v002.R")Data Updates
The latest county-level burden data are downloaded:
readList <- list("usafCase"="./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20230208.csv",
"usafDeath"="./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20230208.csv"
)
compareList <- list("usafCase"=readFromRDS("cty_newdata_20230108")$dfRaw$usafCase,
"usafDeath"=readFromRDS("cty_newdata_20230108")$dfRaw$usafDeath
)
# Use existing clusters
cty_newdata_20230208 <- readRunUSAFacts(maxDate="2023-02-06",
downloadTo=lapply(readList,
FUN=function(x) if(file.exists(x)) NA else x
),
readFrom=readList,
compareFile=compareList,
writeLog="./RInputFiles/Coronavirus/USAF_NewData_20230208_chk_v005.log",
ovrwriteLog=TRUE,
useClusters=readFromRDS("cty_newdata_20210813")$useClusters,
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20230208.csv## Rows: 3193 Columns: 1115
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): County Name, State, StateFIPS
## dbl (1112): countyFIPS, 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: countyFIPS countyName state stateFIPS
##
##
## *** File has been checked for uniqueness by: countyFIPS stateFIPS date## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 34
## Detailed differences available in: ./RInputFiles/Coronavirus/USAF_NewData_20230208_chk_v005.log
##
## Checking for similarity of: county
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## 0 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20230208_chk_v005.log##
##
## ***Differences of at least 0 and at least 0.1%
##
## 0 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20230208_chk_v005.log## Rows: 3193 Columns: 1115
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): County Name, State, StateFIPS
## dbl (1112): countyFIPS, 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: countyFIPS countyName state stateFIPS
##
##
## *** File has been checked for uniqueness by: countyFIPS stateFIPS date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 34
## Detailed differences available in: ./RInputFiles/Coronavirus/USAF_NewData_20230208_chk_v005.log
##
## Checking for similarity of: county
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## 0 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20230208_chk_v005.log##
##
## ***Differences of at least 0 and at least 0.1%
##
## 0 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20230208_chk_v005.log
##
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 4
## isType cases new_cases n
## <chr> <dbl> <dbl> <dbl>
## 1 before 4.90e+10 97284771 3547423
## 2 after 4.84e+10 95083869 3490762
## 3 pctchg 1.20e- 2 0.0226 0.0160
##
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 4
## isType deaths new_deaths n
## <chr> <dbl> <dbl> <dbl>
## 1 before 6.74e+8 1082388 3547423
## 2 after 6.46e+8 1002861 3490762
## 3 pctchg 4.16e-2 0.0735 0.0160## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## NULL
# Plot all counties based on closest cluster
sparseCountyClusterMap(cty_newdata_20230208$useClusters,
caption="Includes only counties with 25k+ population",
brewPalette="viridis"
)
# Save the refreshed file
saveToRDS(cty_newdata_20230208, ovrWriteError=FALSE)Vaccines data are also updated:
cty_vaxdata_20230209 <- processCountyVaccines(loc="./RInputFiles/Coronavirus/county_vaccine_20230209.csv",
ctyList=readFromRDS("cty_newdata_20230208"),
minDateCD=c("2022-06-09", "2022-06-09"),
maxDateCD="2023-01-26"
)## Rows: 414347 Columns: 80
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Date, FIPS, Recip_County, Recip_State, SVI_CTGY, Metro_status
## dbl (74): MMWR_week, Completeness_pct, Administered_Dose1_Recip, Administere...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## Records from other than 50 states and DC:
## # A tibble: 9 × 2
## state n
## <chr> <int>
## 1 AS 126
## 2 FM 127
## 3 GU 252
## 4 MH 126
## 5 MP 126
## 6 PR 9969
## 7 PW 126
## 8 VI 506
## 9 <NA> 81
## Warning: Removed 16 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 16 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 16 rows containing non-finite values (`stat_boxplot()`).##
## Count of NA records by column
## state FIPS popgte65_minpop popgte65_maxpop popgte65_nnA
## 0 0 0 0 0
## n
## 0
##
## Records where minimum and maximum population differ# A tibble: 0 × 5
## # … with 5 variables: state <chr>, FIPS <chr>, age <chr>, minpop <dbl>,
## # maxpop <dbl>
##
##
##
## Will run with parameters:
## burdenVar: cpm dpm
## vaxVar: vxcpoppct vxcpoppct
## minDateCD: 2022-06-09 2022-06-09
## maxDateCD: 2023-01-26 2023-01-26## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>
## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 16 rows containing non-finite values (`stat_smooth()`).## Warning: The following aesthetics were dropped during statistical transformation: weight
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?## Warning: Removed 16 rows containing missing values (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 16 rows containing non-finite values (`stat_smooth()`).## Warning: The following aesthetics were dropped during statistical transformation: weight
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: weight
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: weight
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: weight
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?## Warning: Removed 16 rows containing missing values (`geom_point()`).
##
## Call:
## lm(formula = get(burdenVar) ~ vaxMetric, data = dfReg, weights = pop)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -313131506 -1913551 266283 2800223 168848464
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28983.59 3272.65 8.856 < 2e-16 ***
## vaxMetric 156.60 50.59 3.096 0.00198 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11020000 on 3124 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.003058, Adjusted R-squared: 0.002739
## F-statistic: 9.584 on 1 and 3124 DF, p-value: 0.001981
##
##
## Call:
## lm(formula = get(burdenVar) ~ vaxMetric * type + 0 - vaxMetric,
## data = dfReg, weights = pop)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -313079816 -2168552 -27943 2554658 168198528
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## type<25k 34129.38 11883.21 2.872 0.004105 **
## type>500k 18078.44 7005.12 2.581 0.009904 **
## type100k-500k 26691.52 6982.55 3.823 0.000135 ***
## type25k-100k 30691.16 7874.97 3.897 9.93e-05 ***
## vaxMetric:type<25k 139.46 239.27 0.583 0.560040
## vaxMetric:type>500k 305.47 99.28 3.077 0.002111 **
## vaxMetric:type100k-500k 188.19 112.16 1.678 0.093467 .
## vaxMetric:type25k-100k 138.08 148.62 0.929 0.352936
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11020000 on 3118 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.5674, Adjusted R-squared: 0.5662
## F-statistic: 511.1 on 8 and 3118 DF, p-value: < 2.2e-16## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 16 rows containing non-finite values (`stat_smooth()`).## Warning: The following aesthetics were dropped during statistical transformation: weight
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?## Warning: Removed 16 rows containing missing values (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 16 rows containing non-finite values (`stat_smooth()`).## Warning: The following aesthetics were dropped during statistical transformation: weight
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: weight
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: weight
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: weight
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?## Warning: Removed 16 rows containing missing values (`geom_point()`).
##
## Call:
## lm(formula = get(burdenVar) ~ vaxMetric, data = dfReg, weights = pop)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -3701058 -22105 2245 34856 777649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 486.4319 35.9334 13.537 <2e-16 ***
## vaxMetric -5.1424 0.5554 -9.258 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 121000 on 3124 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.0267, Adjusted R-squared: 0.02639
## F-statistic: 85.71 on 1 and 3124 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = get(burdenVar) ~ vaxMetric * type + 0 - vaxMetric,
## data = dfReg, weights = pop)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -3646748 -29301 -6242 25901 766646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## type<25k 387.020 129.649 2.985 0.002857 **
## type>500k 301.573 76.428 3.946 8.13e-05 ***
## type100k-500k 259.678 76.181 3.409 0.000661 ***
## type25k-100k 420.757 85.918 4.897 1.02e-06 ***
## vaxMetric:type<25k -1.708 2.610 -0.654 0.513066
## vaxMetric:type>500k -2.941 1.083 -2.715 0.006664 **
## vaxMetric:type100k-500k -1.289 1.224 -1.053 0.292391
## vaxMetric:type25k-100k -2.779 1.622 -1.714 0.086621 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 120300 on 3118 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.1849, Adjusted R-squared: 0.1828
## F-statistic: 88.41 on 8 and 3118 DF, p-value: < 2.2e-16# Save the refreshed file
saveToRDS(cty_vaxdata_20230209, ovrWriteError=FALSE)County-level data are post-processed:
cty_postdata_20230208 <- postProcessCountyData(lstCtyBurden=cty_newdata_20230208$dfPerCapita,
lstCtyVax=cty_vaxdata_20230209$vaxFix,
lstState=readFromRDS("cdc_daily_230202")$dfPerCapita,
excludeStates="AK"
)##
## Parameter maxDate is: 2023-02-01## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>
Additional post-processing steps are run:
# Step 1a: Burden comparisons for aggregated states
additionalCountyPostProcess(cty_postdata_20230208, p1CompareStates=c(state.abb, "DC"), p1AggData=TRUE)## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>## Warning: Removed 6 rows containing missing values (`geom_line()`).
# Step 1: Burden aggregation for key states
# Step 2: vaccine comparisons
# Step 3: Scoring updates (and errors)
# Step 4: New rolling data (28-day default with ceilings 50000 CPM, 500 DPM)
additionalCountyPostProcess(cty_postdata_20230208,
p1CompareStates=c("GA", "FL", "NE", "IL", "OR"),
p2VaxStates=c("MA", "HI", "VA", "VT", "RI", "NE"),
p3VaxTimes=sort(c("2022-01-01", "2023-01-25")),
p4DF=cty_newdata_20230208$dfPerCapita,
excludeStates=c("AK")
)## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 379 rows containing missing values (`geom_line()`).
Memory is cleaned:
# List of files
sapply(ls(), FUN=function(x) object.size(get(x))) %>% sort(decreasing=FALSE)## usafUpdatedURL usafMainURL
## 168 232
## rawMakeVarMapper readList
## 592 832
## zeroNA fullListMapper
## 840 848
## zeroPad2 zeroPad5
## 896 896
## pivotMapper checkControlGroupMapper
## 1024 1056
## plotSimilarityMapper glimpseFile
## 1056 1064
## checkControlVarsMapper fileRead
## 1344 1344
## uqMapper perCapMapper
## 1400 1408
## urlMapper lstExcludeMapper
## 1488 1544
## combineFiles vecSelectMapper
## 2072 2160
## colSelector glimpseLog
## 2184 2688
## zeroPad customYYYYMM
## 2744 2856
## vecToTibble genNewLog
## 3192 3272
## sumImputedHHS renMapper
## 3368 3488
## helperRollingAgg lstComboMapper
## 3584 3624
## pivotData fileDownload
## 4256 4264
## skinnyHHS createBurdenCountyDate
## 4272 4480
## postProcessCDCDaily processCountyVaccines
## 4480 4496
## readFromRDS printLog
## 4720 4856
## joinFrames getStateData
## 5040 5272
## lagCorrCheck checkUniqueRows
## 5488 5536
## helperPerCapita rowFilter
## 5824 6216
## colMutater cleanMem
## 6272 6408
## getClusters checkSimilarityMapper
## 6832 6912
## onePageCFRPlot getCountyClusters
## 6928 7280
## saveToRDS lstFilterMapper
## 7608 7800
## specSumProd helperLinePlot
## 7896 8152
## colRenamer clustersToFrame
## 8176 8744
## specNA helperMakePerCapita
## 8888 9016
## checkControl createGroupAgg
## 9192 9296
## getCountyData testImputeNA
## 9608 9632
## integrateData createPerCapita
## 10304 10512
## combineAggData imputeNACapacity
## 10864 11016
## pivotStateBurdenData createRestatementData
## 11120 11208
## helperSimilarity plotSimilarity
## 11448 11488
## findPeaks createVaxBurdenData
## 11984 12496
## integrateStateVaccine makeBurdenSummary
## 12768 13736
## combineRows createSummedCountyBurdenData
## 13776 13968
## findDeltaFromMax makeCaseHospDeath
## 14000 14560
## checkSimilarity clusterCounties
## 14736 15072
## helperAggTrend selfListMapper
## 17232 17240
## diagnoseClusters processRawFile
## 17472 18480
## flagLargeDelta plotByRestatement
## 18992 19560
## cumulativeBurdenPlot additionalCountyPostProcess
## 19728 20304
## postProcessCountyData helperAggTotal
## 20328 20448
## tempStackPlot hospitalCapacityCDCDaily
## 20736 20792
## scoreVaxSimilarity sparseCountyClusterMap
## 20968 21072
## stateAgeVaxEvolution repairVaxPopulation
## 21560 22264
## downloadCountyVaccines createGeoMap
## 22528 24880
## corrVaxBurden helperElbow
## 25096 25592
## keyAggMapper downloadReadHospitalData
## 26624 27312
## plotVaxBurdenData plotCombineAggByMapper
## 27368 28552
## plotDeltaFromMax makeBurdenDatePlot
## 28992 29048
## compareAggregate compareStateSummedCounty
## 29776 30616
## filterPopStateAge hospAgePerCapita
## 31616 31632
## scoreSimilarity plotCFRLag
## 32152 32648
## helperSummaryMap createSummary
## 32800 33424
## readQCRawCDCDaily findCorrAlign
## 33816 35104
## readRunCDCDaily cumulativeVaccinePlot
## 35624 36752
## readRunUSAFacts plotHospitalUtilization
## 38008 38224
## countyCorr readQCRawUSAF
## 39064 42776
## readPopStateAge createBurdenPivot
## 43456 44824
## peakValleyCDCDaily makePeakValley
## 46208 50344
## clusterStates bucketPopStateAge
## 53696 54760
## createDetailedSummaries cty_postdata_20230208
## 77552 25101640
## cty_vaxdata_20230209 compareList
## 66616336 385776176
## cty_newdata_20230208
## 1566152592# Clean large objects
largeObjs <- c("cty_newdata_20230208", "cty_vaxdata_20230209")
cleanMem(largeObjs, delObjs=TRUE)##
## Memory usage prior to deleting files:
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 1083495 57.9 1912370 102.2 1373942 73.4
## Vcells 320673436 2446.6 638815820 4873.8 638626468 4872.4
##
## Memory usage after deleting files:
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 971998 52.0 1912370 102.2 1373942 73.4
## Vcells 52861121 403.3 511052656 3899.1 638626468 4872.4# List of files
sapply(ls(), FUN=function(x) object.size(get(x))) %>% sort(decreasing=FALSE)## usafUpdatedURL largeObjs
## 168 224
## usafMainURL rawMakeVarMapper
## 232 592
## readList zeroNA
## 832 840
## fullListMapper zeroPad2
## 848 896
## zeroPad5 pivotMapper
## 896 1024
## checkControlGroupMapper plotSimilarityMapper
## 1056 1056
## glimpseFile checkControlVarsMapper
## 1064 1344
## fileRead uqMapper
## 1344 1400
## perCapMapper urlMapper
## 1408 1488
## lstExcludeMapper combineFiles
## 1544 2072
## vecSelectMapper colSelector
## 2160 2184
## glimpseLog zeroPad
## 2688 2744
## customYYYYMM vecToTibble
## 2856 3192
## genNewLog sumImputedHHS
## 3272 3368
## renMapper helperRollingAgg
## 3488 3584
## lstComboMapper pivotData
## 3624 4256
## fileDownload skinnyHHS
## 4264 4272
## createBurdenCountyDate postProcessCDCDaily
## 4480 4480
## processCountyVaccines readFromRDS
## 4496 4720
## printLog joinFrames
## 4856 5040
## getStateData lagCorrCheck
## 5272 5488
## checkUniqueRows helperPerCapita
## 5536 5824
## rowFilter colMutater
## 6216 6272
## cleanMem getClusters
## 6408 6832
## checkSimilarityMapper onePageCFRPlot
## 6912 6928
## getCountyClusters saveToRDS
## 7280 7608
## lstFilterMapper specSumProd
## 7800 7896
## helperLinePlot colRenamer
## 8152 8176
## clustersToFrame specNA
## 8744 8888
## helperMakePerCapita checkControl
## 9016 9192
## createGroupAgg getCountyData
## 9296 9608
## testImputeNA integrateData
## 9632 10304
## createPerCapita combineAggData
## 10512 10864
## imputeNACapacity pivotStateBurdenData
## 11016 11120
## createRestatementData helperSimilarity
## 11208 11448
## plotSimilarity findPeaks
## 11488 11984
## createVaxBurdenData integrateStateVaccine
## 12496 12768
## makeBurdenSummary combineRows
## 13736 13776
## createSummedCountyBurdenData findDeltaFromMax
## 13968 14000
## makeCaseHospDeath checkSimilarity
## 14560 14736
## clusterCounties helperAggTrend
## 15072 17232
## selfListMapper diagnoseClusters
## 17240 17472
## processRawFile flagLargeDelta
## 18480 18992
## plotByRestatement cumulativeBurdenPlot
## 19560 19728
## additionalCountyPostProcess postProcessCountyData
## 20304 20328
## helperAggTotal tempStackPlot
## 20448 20736
## hospitalCapacityCDCDaily scoreVaxSimilarity
## 20792 20968
## sparseCountyClusterMap stateAgeVaxEvolution
## 21072 21560
## repairVaxPopulation downloadCountyVaccines
## 22264 22528
## createGeoMap corrVaxBurden
## 24880 25096
## helperElbow keyAggMapper
## 25592 26624
## downloadReadHospitalData plotVaxBurdenData
## 27312 27368
## plotCombineAggByMapper plotDeltaFromMax
## 28552 28992
## makeBurdenDatePlot compareAggregate
## 29048 29776
## compareStateSummedCounty filterPopStateAge
## 30616 31616
## hospAgePerCapita scoreSimilarity
## 31632 32152
## plotCFRLag helperSummaryMap
## 32648 32800
## createSummary readQCRawCDCDaily
## 33424 33816
## findCorrAlign readRunCDCDaily
## 35104 35624
## cumulativeVaccinePlot readRunUSAFacts
## 36752 38008
## plotHospitalUtilization countyCorr
## 38224 39064
## readQCRawUSAF readPopStateAge
## 42776 43456
## createBurdenPivot peakValleyCDCDaily
## 44824 46208
## makePeakValley clusterStates
## 50344 53696
## bucketPopStateAge createDetailedSummaries
## 54760 77552
## cty_postdata_20230208 compareList
## 25101640 385776176The latest county-level burden data are downloaded:
readList <- list("usafCase"="./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20230308.csv",
"usafDeath"="./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20230308.csv"
)
compareList <- list("usafCase"=readFromRDS("cty_newdata_20230208")$dfRaw$usafCase,
"usafDeath"=readFromRDS("cty_newdata_20230208")$dfRaw$usafDeath
)
# Use existing clusters
cty_newdata_20230308 <- readRunUSAFacts(maxDate="2023-03-06",
downloadTo=lapply(readList,
FUN=function(x) if(file.exists(x)) NA else x
),
readFrom=readList,
compareFile=compareList,
writeLog="./RInputFiles/Coronavirus/USAF_NewData_20230308_chk_v005.log",
ovrwriteLog=TRUE,
useClusters=readFromRDS("cty_newdata_20210813")$useClusters,
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20230308.csv## Rows: 3193 Columns: 1115
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): County Name, State, StateFIPS
## dbl (1112): countyFIPS, 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: countyFIPS countyName state stateFIPS
##
##
## *** File has been checked for uniqueness by: countyFIPS stateFIPS date## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `across(.cols = all_of(useVars), .fns = fn, ...)`.
## ℹ In group 1: `date = 2020-01-22`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 0
## Detailed differences available in: ./RInputFiles/Coronavirus/USAF_NewData_20230308_chk_v005.log
##
## Checking for similarity of: county
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## 0 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20230308_chk_v005.log## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.## Warning in left_join(., ref, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.##
##
## ***Differences of at least 0 and at least 0.1%
##
## 0 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20230308_chk_v005.log
##
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20230308.csv## Rows: 3193 Columns: 1115
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): County Name, State, StateFIPS
## dbl (1112): countyFIPS, 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: countyFIPS countyName state stateFIPS
##
##
## *** File has been checked for uniqueness by: countyFIPS stateFIPS date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 0
## Detailed differences available in: ./RInputFiles/Coronavirus/USAF_NewData_20230308_chk_v005.log
##
## Checking for similarity of: county
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## 0 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20230308_chk_v005.log## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.##
##
## ***Differences of at least 0 and at least 0.1%
##
## 0 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20230308_chk_v005.log
##
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 4
## isType cases new_cases n
## <chr> <dbl> <dbl> <dbl>
## 1 before 4.90e+10 97284771 3547423
## 2 after 4.84e+10 95083869 3490762
## 3 pctchg 1.20e- 2 0.0226 0.0160
##
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 4
## isType deaths new_deaths n
## <chr> <dbl> <dbl> <dbl>
## 1 before 6.74e+8 1082388 3547423
## 2 after 6.46e+8 1002861 3490762
## 3 pctchg 4.16e-2 0.0735 0.0160## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## NULL
# Plot all counties based on closest cluster
sparseCountyClusterMap(cty_newdata_20230308$useClusters,
caption="Includes only counties with 25k+ population",
brewPalette="viridis"
)
# Save the refreshed file
saveToRDS(cty_newdata_20230308, ovrWriteError=FALSE)Data on USA Facts have not been updated since February 5, so this process may have run its course